Blind AI Evaluation: Removing Brand Bias from Model Scoring
If you know which model wrote an answer, you can't score it fairly. Blind evaluation fixes that — here's how.
The bias problem
People — and evaluators — carry expectations about brands. If you know an answer came from a famous model, you tend to rate it higher; if it came from an underdog, lower. That halo effect quietly corrupts any comparison where the source is visible.
The result is a verdict that measures reputation as much as quality.
How blind judging works
Blind evaluation removes the label before scoring. Every answer is shuffled and anonymized — relabeled as “Response A, B, C” — so the judge rates the text with no idea which model produced it.
Each answer is then scored against a transparent rubric on a simple scale, one line at a time. Only after scoring are the labels revealed. The score reflects the answer on its merits, not the logo attached to it.
Why it matters for trust
Blind judging is what makes a multi-model comparison trustworthy rather than a popularity contest. It's also why disagreement between models is meaningful: when anonymized answers still diverge, the split is about substance, not branding.
Ask Quor runs every comparison this way — anonymize, score against a readable rubric, then reveal — so the verdict you get is about the answer, not the name.